Repo for my graduate data science machine learning class at UCSD (UC San Diego). This course provides a broad introduction to the practical side of machine-learning and data analysis. The topics covered in this class include topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting and perceptrons, and topics in unsupervised learning, such as k-means, PCA and Gaussian mixture models.

epicurious_recipes

Machine Learning Study on a Dataset of Epicurious Recipes

fonts

Font files available from Google Fonts

ipython-notebooks

Informal IPython experiments and tutorials. TensorFlow, machine learning/deep learning/RL, NLP applications.

k-nearest-neighbors

Exercise on K-Nearest Neighbor using Scikit-Learn

keras-deep-learning-tutorials

machine-learning-regression

Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

machine-learning-with-python

Python codes for common Machine Learning Algorithms

machine_learning_basics

Plain python implementations of basic machine learning algorithms

mysql-learning-repo

The repository for all of my MySQL learning exercises, practiced using the AdventureWorks and Sakila question sets from SQLZoo and MySQL.com, respectively.